The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for... (more)

Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of... (more)

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About TAAS

ACM Transactions on Autonomous and Adaptive Systems (TAAS) is a venue for high-quality research contributions addressing foundational, engineering, and technological aspects related to all those complex ICT systems that have to serve - in autonomy and with capabilities of autonomous adaptation - in highly dynamic socio-technico-physical environments.

Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. However, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction up to 10.8 fold with low computational and communication costs.

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of inter-connected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources, and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfils its goals with minimal profiling overhead.

With robots entering the world of Cyber Physical Systems (CPS), ordering the execution of allocated tasks during run-time becomes crucial. This is so because, in a real world, there can be several physical tasks that use shared resources that need to be executed concurrently. In this paper, we propose a mechanism to solve this issue of ordering task executions within a CPS which inherently handles mutual exclusion. The mechanism caters to a decentralized and distributed CPS comprising nodes such as computers, robots and sensor nodes, and uses mobile software agents that knit through them to aid the execution of the various tasks while also ensuring mutual exclusion of shared resources. The computations, communications and control, are achieved through these mobile agents. Physical execution of the tasks is performed by the robots in an asynchronous and pipelined manner without the use of a clock. The mechanism also features addition and deletion of tasks and insertion and removal of robots facilitating On-The-Fly Programming. As an application, a Warehouse Management System as a CPS has been implemented. The paper concludes with the results and discussions on using the mechanism in both emulated and real world environments.

Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfil its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap.
Given the opportunities in the field, the main contributions of this work are twofolds: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.

In multiagent systems, social norms serves as an important technique in regulating agents' behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this paper, we propose two novel learning strategies under the collective learning framework: \emph{collective learning EV-l} and \emph{collective learning EV-g}, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.

Virtualization of resources in cloud computing has enabled developers to commission and recommission resources at will and on demand. This virtualization is a coin with two sides. On one hand, the flexibility in managing virtual resources has enabled developers to efficiently manage their costs; they can easily remove unnecessary resources or add resources temporarily when the demand increases. On the other hand, the volatility of such environment and the velocity with which changes can occur may have a greater impact on the economic position of a stakeholder and the business balance of the overall ecosystem. In this work, we recognize the business ecosystem of cloud computing as an economy of scale and explore the effect of this fact on decisions concerning scaling the infrastructure of web applications to account for fluctuations in demand. The goal is to reveal and formalize opportunities for economically optimal scaling that takes into account not only the cost of infrastructure, but also the revenue from service delivery and eventually the profit of the service provider. The end product is a scaling mechanism that makes decisions based on both performance and economic criteria and takes adaptive actions to optimize both performance and profitability for the system.

Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This paper examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed theory and myriads of successful applications. The paper focuses on modelling the dynamic relationship between requirements and possible adaptations. It then proposes a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost-function. This is accomplished through a model-based framework for designing self-adaptive software systems that can guarantee a certain level of requirements satisfaction over time, by dynamically composing adaptation strategies when necessary. The proposed framework is illustrated and evaluated through two simulated systems, namely the Meeting-Scheduling exemplar and an E-Shop.

In this paper, we explore the efficacy of dynamic effective capacity modulation (i.e., using virtualization techniques to offer lower resource capacity than that advertised by the cloud provider) as a control knob for a cloud provider's profit maximization complementing the more well-studied approach of dynamic pricing. In particular, our focus is on emerging cloud ecosystems wherein we expect tenants to modify their demands strategically in response to such modulation in effective capacity and prices. Towards this, we consider a simple model of a cloud provider that offers a single type of virtual machine to its tenants and devise a leader/follower game-based cloud control framework to capture the interactions between the provider and its tenants. We assume both parties employ myopic control and short-term predictions to reflect their operation under the high dynamism and poor predictability in such environments. Our evaluation using a combination of real data center traces and real-world benchmarks hosted on a prototype OpenStack-based cloud shows 10-30% profit improvement for a cloud provider compared with baselines that use static pricing and/or static effective capacity.